David's Blog

On this page, I'll share my thoughts, and any articles or information I think are of interest.  Feel free to use the comments section to join in the discussion!


“Here’s the math”

 

 

 

California poised to win Super Bowl ‘jock tax’

By Kathleen Pender on February 5, 2016 at

Here’s one Super Bowl wager you can’t lose: Bet on the Franchise Tax Board.  Thanks to California’s sky-high tax rate and the so-called “jock tax,” the state will score an income tax windfall on the seven-day football extravaganza no matter which team wins.

California is one of multiple states that tax a percentage of visiting athletes’ income based on how many “duty days” they are in the state.

California’s “sports program” defines a duty day as “any day services are performed under the contract from the beginning of an official preseason activity until the last game played. The duty days in California are then divided by the total duty days to create a ratio. This ratio is then multiplied by the total compensation. This then is deemed to be the California source income.”

California’s top income tax rate is 13.3 percent, the highest of any state.

Forbes estimated the tax hit this will put on Panthers’ quarterback Cam Newton.

Here’s the math: In 2016, Newton will spend 11 duty days in California, including seven in San Francisco for the Super Bowl. That represents about 5.3 percent of his total estimated 206 duty days.

Newton’s total football income this year, including his base salary, signing bonus and playoff bonuses, totals about $23 million. He will get an extra $102,000 if the Panthers win the Super Bowl or $51,000 if they lose.

If you subject 5.3 percent Newton’s total income for the year to California’s state income tax, Forbes estimated he will pay California a total of $159,560 in 2016 taxes if his team wins and $159,200 if it loses.

David Hersch, a CPA and partner with Armanino, ran the numbers and got a slightly different tax.  He estimated that Newton will owe $149,000 in California income tax if the Panthers win. (This ignores any credit he could take against his home-state taxes for taxes paid in California.)

If the Panthers hadn’t made the Super Bowl, Newton would only owe about $55,000 in California tax, assuming four duty days in California out of an estimated 199 total, Hersch said.

So if he plays in the Super Bowl, Newton pays $94,000 more in California tax, but he also gets a $102,000 bonus if the Panthers or $51,000 if they lose. “For him, if they win it’s break-even,” Hersch said. If the Broncos win, “he’s losing yardage.” 

That’s a gamble any player would take.

“There’s not a player on either team who wouldn’t pay to play in this game because they are fulfilling a dream they have had ever since they they picked up a football,” said Joe McLean, a managing partner with Intersect Capital, a wealth management firm that represents athletes. Getting a big ole Super Bowl ring also enhances their lifetime endorsement potential and puts them in line for a job with the team when they’re done playing. “When you put that in perspective, the jock tax is minimal,” McLean said.

 

http://blog.sfgate.com/pender/2016/02/05/california-poised-to-win-super-bowl-jock-tax/

 

“When drafting a pitcher, high school or college, you’re taking a likely failure, even at the very top of the draft. “

 

 

 

A Quick Thought on the 2016 #1 Pick

by Dave Cameron - February 6, 2016

Earlier this week, I read an article on Philly.com about Jason Groome, a left-handed high school pitching prospect thought to be in the mix for the first overall pick in this upcoming draft. In addition to being the local-ish prospect, Groome got a stamp of approval from Cole Hamels, who saw him do a workout for the Phillies last year before Hamels was traded to Texas.

“He did a workout with the Phillies,” Hamels said. “I was there, throwing a bullpen, and he had just got done throwing. I was like, ‘How old are you?’

Hamels, 32, laughed.

“I was not that big,” he said. “Gavin Floyd and I were skinny. We didn’t throw that hard. We had to grow into our bodies to get some velocity. He’s already got it. I told him, man, you stay healthy and you are going to be golden.”

Of course, given the track record of high school pitchers being selected at the top of the draft, the most important part of that statement is probably the caveat: stay healthy. The history of high school pitchers being taken #1 overall is, of course, terrible: David Clyde and Brien Taylor were such notable busts that teams went 23 years without selecting a high school arm with the top pick again, and when the Astros bucked the trend to select Brady Aiken with the top pick in 2014, that didn’t exactly go their way either; he ended up not signing after a dispute over his medicals, and required Tommy John surgery before getting re-drafted #17 overall by Cleveland last year.

Even expanding the sample to high school arms taken in the top three — since it’s possible that teams with the #1 overall pick were being too risk averse during the 1991-2014 window, and some elite pitchers who went #2 or #3 could have gone #1 overall — the results don’t get much more optimistic. 11 high school arms have been taken with the #2 overall pick in the draft, with Josh BeckettBill Gullickson, and J.R. Richard the only three to have real big league careers. Adding in the nine high school pitchers taken with the third pick gets you Steve AveryJoe Coleman, and Larry Christensen, and that’s the success stories; the other six didn’t do anything in the big leagues.

The failure rate of high school pitching prospects has been well chronicled, and teams have mostly shifted away from using top-of-the-draft selections on high school arms because of the risks. And with the Phillies embracing an analytical approach under new GM Matt Klentak — they just hired Andy Galdi away from Googleto run their research and development team — it might seem like the common wisdom would suggest that they should lean towards one of the two college arms — A.J. Puk or Alec Hansen — who are also considered potential options for the top pick at this point. After all, one of the main narratives in “Moneyball” was the A’s eschewing of high school talent in favor of closer-to-the-majors college players.

But it’s also worth noting that the game has changed dramatically since “Moneyball” was published, and the idea that an analytical approach to the draft equals “take college guys” isn’t really true anymore. After all, the Astros are one of the most aggressively analytical teams in baseball, and they’re the ones who took a high school pitcher #1 overall, breaking the 23 year drought. And it probably wasn’t a crazy selection, even though it didn’t go that well for them that year. As Ben Lindbergh noted in his pre-draft piece last year, the game has significantly reversed course on the trend towards drafting college players in the last few years. Borrowing a chart from his piece:

 

You don’t have to be a data scientist from Google to see that the sport decided they were too college-heavy a few years ago, and have moved back towards something closer to a 50/50 split between high school and college draftees. The trend in the percentage of overall selections shouldn’t drive the decision on who to take at the very top of the draft, but it is worth noting that the teams themselves seem to believe they overcompensated towards college players when evaluating the risk/reward incentive after “Moneyball” came out. Given that teams have significantly better data on amateur players than the public has, this kind of trend suggests that the industry seems to have figured something out, and it’s possible that high school players represent a better bet now than they have in the past.

And given a draft class that looks to offer (at this point) no clear option for a hitter at the top of the draft, the Phillies are going to be picking between guys who are all injury risks. Even college arms blow out with alarming frequency, and describing guys like Puk or Hansen as safe selections ignores the history of guys like Mark AppelDanny HultzenGreg Reynolds, and Trevor Bauer. When drafting a pitcher, high school or college, you’re taking a likely failure, even at the very top of the draft.

College pitchers succeed more often — in addition to guys like Carlos Rodon andGerrit Cole in the last five years, colleges also get to boast David Price andStephen Strasburg a few years before that — but neither Puk nor Hansen seems to be the kind of consensus top selection that those guys were, and equating the best college arms in a weak draft class with past elite prospects simply because they’re also pitching for NCAA schools seems intellectually dishonest.

So for the second time in three years, there seems to be a decent chance that a high school pitcher could go #1 overall, after having it not happen for over two decades. And given that there doesn’t appear to be any real low-risk option on the board this year, it might not be entirely crazy.

http://www.fangraphs.com/blogs/a-quick-thought-on-the-2016-1-pick/

 

"With sports, and especially baseball, a wealth of data allows us to measure productivity with a precision not available in most industries."

February 5, 2016

Caught Looking

Two New Looks at Umpires and Racial Bias

by Michael Wenz

Caught Looking examines articles from the academic literature relevant to baseball and statistical analysis. This review covers three articles on the topic of racial discrimination by umpires. The goal, as always, is to expose the academic frontier to a wide audience and seek ways to move the discussion forward.

Sports can sometimes provide an interesting laboratory to examine pressing social questions that are hard to analyze in other places. It’s hard to measure whether two accountants have different salaries because they have different productivity levels or because one might face discrimination in the labor market. With sports, and especially baseball, a wealth of data allows us to measure productivity with a precision not available in most industries.

This past year, two papers in the Journal of Sports Economics investigated the possibility of racial discrimination by umpires in Major League Baseball using datasets that covered millions of pitches. The first paper, Further Examination of Potential Discrimination Among MLB Umpires, by Scott Tainsky, Brian Mills and Jason Winfree, focused on data from 1997-2008, and the second, The Connection Between Race and Called Strikes and Balls, by Jeff Hamrick and John Rasp, drew on data from 1989-2010. Both of these papers examined carefully a 2011 paper that appeared in the American Economic Review, the top journal in the economics literature. Strike Three: Discrimination, Incentives, and Evaluation, by Christopher Parsons, Johan Sulaeman, Michael Yates and Daniel Hamermesh, focused on the period 2004-2008.

Parsons and his co-authors found some evidence of discrimination by umpires against pitchers of a different race, but that this effect disappears the more carefully scrutinized the umpire’s decision is. While this effect is statistically significant, it is also small, affecting on average something less than one pitch per team per game. In both of the more recent papers, the authors found that any evidence of racial discrimination by umpires is very dependent on the particular modeling assumptions.

Before digging into what causes the differences in results, it’s useful to look first at the goals of the first study. Parsons and his co-authors had a somewhat more ambitious goal than to simply identify whether there was discrimination among umpires. They also wanted to see whether pitchers and batters changed their behavior in response to this. In other words, if a pitcher found himself on the wrong end of a discriminating umpire, he may be more likely to try to miss bats with fastballs than try to sneak a back-door breaking ball on the black. In addition, they also asked whether umps tried to get away with discrimination more often when they weren’t being as closely watched, either by fans or by the newly invented QuesTec machines, and whether pitchers and batters were adjusting accordingly.

To recap, the authors hypothesized that umpires engaged in racial discrimination, that players were able to recognize that this discrimination existed, that players were able to recognize that the extent of discrimination was different under different conditions of monitoring, and that players adjusted their behavior in the face of discrimination. This is a lot to ask of data, even with millions of observations, and it is remarkable that they found some support all the way through this logical chain.

Their paper focused on umpire-pitcher discrimination, rather than umpire-batter discrimination, and began by classifying umpires and pitchers as White, Black, Hispanic or Asian, then breaking down the called-strike percentage of taken pitches by different racial combinations. After examining over 3.5 million pitches, a little over half of which were called balls or strikes by the umpire, they found a slightly higher called-strike percentage (32.0 percent to 31.5 percent) when the umpire and pitcher are racially matched than when they are not. Next, they employed a linear probability model to account for differences in game situations (inning, count, home field) and umpire-, pitcher- and batter-fixed effects (more on this later), to isolate the effect of umpire discrimination on called strike probabilities. There is some evidence that Hispanic umpires may favor Hispanic pitchers relative to White pitchers, and some evidence that White umpires favor White pitchers over Hispanic and Black pitchers, but the magnitude is small. In the full model, the authors do not find that their estimates for discrimination are statistically significant.

However, they press on. Perhaps close monitoring changes the behavior of umpires—if you know you’re being watched more carefully, you discriminate at a different time. Alternatively, perhaps racial bias changes the behavior of pitchers—if you know you’re getting squeezed on the corners, you don’t throw to the corners. And here they find evidence that, in fact, people are changing their behaviors. In the period of study, QuesTec cameras were installed in some, but not all, ballparks to track pitches. Without QuesTec in the park, umpires are 0.59 percentage points more likely to call a strike for a racially matched pitcher, but the presence of QuesTec reduces the probability by 1.07 percentage points, more than offsetting the racial bias found without QuesTec. Both effects are statistically significant. They find similar results for highly attended games and on terminal (two-strike or three-ball) counts where implicit scrutiny is likely to be high. In addition, PITCHf/x data on pitch location from the 2007-2008 seasons shows that racially matched pitchers do in fact throw more often to the edges of the strike zone in non-QuesTec parks, and that racially matched pitchers give up fewer hits in non-QuesTec parks. Together, all of these results suggest that racial differences between pitchers and umpires impact behavior, and that monitoring reduces or eliminates its effects.

This is fascinating, and even in datasets with millions of observations like we have with pitches, this is a hard thing to find. However, these results come at the end of a long logical chain and depend on the first result—racial discrimination among umpires—being present. The two more recent papers call this into question.

Hamrick and Rasp use data for 32 seasons, from 1989-2010, covering more than 13 million pitches. They also focus on umpire-batter matching, not just umpire-pitcher matching, and find a mix of contradictory information even in their descriptive statistics. For example, White umpires call more strikes both for White pitchers relative to Blacks and Hispanics, but also call relatively more strikes against White hitters. The empirical strategy is to use two-way and three-way interaction terms to identify racial discrimination. Three-way interaction terms combine dummy variables for umpire race, pitcher or hitter race, and situation (terminal count, QuesTec, attendance, game score). In the full version of their model, none of the three-way interaction effects are statistically significant, though they suppress the actual coefficient estimates which would show whether the signs and magnitudes are consistent with what would be expected. From here, they drop the three-way interaction terms and revisit the model with two-way interaction terms.

In this version of the model, the authors find a number of statistically significant differences in behavior across races that appear in various circumstances—for instance, Latin umpires call fewer strikes when QuesTec is present. But note that the three-way indicator was not significant, implying that this behavior is roughly consistent across races of pitchers and batters. There are a number of other situations where umpire race and called strike probability are statistically significantly related, but again, independent of the race of the pitcher or batter.

Hamrick and Rasp suggest that these racial differences in behavior may lead to spurious findings of racial discrimination in other studies. They don’t refer to the Parsons study directly here, but it would seem to apply.

Tainsky, Mills and Winfree use data from 1997-2008 on umpire and pitcher races, testing both the whole period and the 2004-2008 period used in the Parsons study. This paper uses a much smaller set of control variables—simply, an indicator for home park and an indicator for QuesTec to go along with the variables of interest indicator for racial match between pitcher and umpire, and an interaction between the QuesTec and racial match indicators. In models without fixed-effects, the authors find a statistically significant effect of discrimination of a magnitude consistent with the other studies, about a half of a percentage point. They also find that the presence of QuesTec partially offsets this effect. However, they do not find evidence of statistically significant discrimination in models including fixed effects for year, pitcher and umpire. This stands in contrast to the earlier study, which found discrimination in models with fixed effects.

In an appendix to their paper, Tainsky, Mills and Winfree dig into the differences between the two papers, and there are several. First, racially classifying umpires and players is hard, especially in the case of Black Hispanic individuals. Laz Diaz, for instance, is responsible for about a tenth of pitches called by minority umpires, and the two datasets arrive at different classifications. A number of other corrections and distinctions are identified as well.

Second, and more importantly in terms of the difference in results, the two models treat fixed effects differently. Fixed effects, in a nutshell, are like player-specific or umpire-specific constants. It’s deeper than that, but basically, different umpires have at least slightly different strike zones and called-strike probabilities, but with fixed-effects, the umpire’s unique fingerprint is held constant from game to game. The same is true for pitchers—some pitchers simply have an approach that leads to more called strikes than others. It seems highly appropriate to include fixed effects for pitchers and umpires when modeling for discrimination. However, as the appendix points out, the two papers treat fixed effects differently. Tainsky and his collaborators use a single pitcher-fixed effect and find no evidence of discrimination, while Parsons and co-authors use two fixed effects for each pitcher and each umpire, one in QuesTec parks and one in non-QuesTec parks, and with this specification, they turn up evidence of racial discrimination.

Tainsky, Mills and Winfree clearly prefer one fixed effect per pitcher, but it’s easy to see why Parsons, Sulaeman, Yates and Hamermesh may prefer two. QuesTec was controversial at its adoption, and there appears to have been at least some conscious impact of the system on player and umpire behavior. All of the studies showed an increase in called-strike probability in QuesTec stadiums, so something changed in QuesTec games versus non-QuesTec games, either in the approach of pitchers, umpires, or both. It’s also quite possible that not all players or umpires changed in uniform fashion, rather that some adjusted differently to QuesTec than others. In other words, the fixed effect for an individual might be different from QuesTec parks to non-QuesTec parks.

The Tainsky study also looks at PITCHf/x data to map the actual called strike zone and test whether pitchers are more likely to throw to the edges of the zone in the presence of a racially matched umpire. Plots of strike zones developed from a smoothed Generalized Additive Model (see the paper for details and cool diagrams) in stadiums with and without QuesTec look nearly identical for both matched and non-matched pitcher-umpire pairs. With these maps, they do find evidence that pitchers tease the edges of the zone in two-strike counts, but they find no evidence in support of discrimination-induced changes in pitcher behavior in regression models.

The Parsons study turns up some interesting and seemingly robust results that depend on a complex series of interactions. Umpires first must discriminate, but only in some instances. Players must recognize this and must find it beneficial to change their behavior accordingly, but again, only in some instances. Discrimination, though, is hard to recognize in the data, even when looking at millions of pitches over decades. Perhaps pitchers can recognize early in games when they’re getting squeezed and adjust within the game, though this seems hard to imagine when discrimination seems to affect about one pitch per game per team. Perhaps minority pitchers whose tendency is to challenge hitters in high-leverage situations perform better in the minor leagues and thus are self-selected into the majors, but this wouldn’t seem to affect their behavior in QuesTec versus non-QuesTec parks.

Looking forward, PITCHf/x provides a new set of tools to investigate the issue of discrimination at the umpire level. None of the studies takes up this issue directly, but game-day scouting of a particular umpire’s tendencies remains a plausible path toward the behaviors and outcomes identified in the Parsons study. Unfortunately for researchers, but perhaps fortunately for fairness, the PITCHf/x strike zone maps we can create these days represent the information needed to identify discrimination if it exists, but also the kind of monitoring that would drive it away.

http://www.baseballprospectus.com/article.php?articleid=28378

 

“The intent of this article is to show some interesting visuals on the aforementioned slider and perhaps bring to the surface an interesting tidbit or two.”

Too many pictures to copy here but click the link to read a good article

 

http://www.hardballtimes.com/the-slider-a-visual-analysis/

“I was really naive. I thought I was close to ready to pitch in the big leagues, but I obviously wasn’t. I had a lot to learn.”

 

A few weeks ago, this column included the story of how Tampa Bay pitching prospect Taylor Guerrieri met hockey legend Bobby Orr on a golf course. Today, we catch a glimpse of Guerrieri’s development, both on the mound and as a person.

For those of you unfamiliar with his background, the 23-year-old right-hander was drafted 24th overall by the Rays in 2011 out of a Columbia, South Carolina high school. Two years later, he underwent Tommy John surgery. While rehabbing, he received a 50-game suspension for a drug of abuse.

“That forced me to grow up,” Guerrieri told me earlier this month. “Coming out of high school, I wasn’t really ready for everything I was handed. And I think I showed that. I was immature. Young athletes sometimes make bad decisions, and when you do, you end up suffering the consequences. I’ve moved past that now.”

He’s also moved past the injury. Back at full strength, Guerrieri logged a 1.85 ERA this past season in 20 games between high-A Charlotte and Double-A Montgomery. Pleased with his progress, the Rays added him to their 40-man roster.

Guerrieri has plus stuff, but he doesn’t envision himself a power pitcher. Featuring a classic three-pitch mix — fastball, curveball, changeup — he “tries to move the ball around and hit the corners.” His low-90s fastball is a two-seamer, so he primarily focuses on keeping the ball down in the zone.

“I grew up throwing a two-seamer,” explained Guerrieri. “It’s usually the other way around — most guys grow up throwing a four-seam — but a two-seam has always felt more comfortable in my hand. What I do is put my middle finger and index finger inside the two seams, so my fingers are actually touching together. I usually get pretty good sink.”

When he was drafted, he pictured himself a shooting star. A teenager with a first-round pedigree, he expected to reach Tampa in short order.

“I thought I’d be there in two years,” admitted Guerreri. “I think I even said that in an interview one time. I was really naive. I thought I was close to ready to pitch in the big leagues, but I obviously wasn’t. I had a lot to learn.”

http://www.fangraphs.com/blogs/sunday-notes-fife-guerrieri-swanson-braves-brewers-twins-more/